def xyzr2xyz(xyzr: np.ndarray,
*,
axis: int = None,
illuminant: Illuminant = None,
observer: Observer = None) -> np.ndarray:
"""
Convert normalized XYZ to LAB
:param xyzr:
:param axis:
:param illuminant:
:param observer:
:return:
"""
if axis is None:
axis = get_matching_axis(xyzr.shape, 3)
if illuminant is None:
illuminant = get_default_illuminant()
if observer is None:
observer = get_default_observer()
new_shape = tuple(-1 if dim == axis else 1
for dim in range(len(xyzr.shape)))
white_point = illuminant.get_white_point(observer).reshape(new_shape)
return xyzr * white_point
def __init__(
self,
data: ArrayLike,
*,
axis: int = None,
illuminant: Illuminant = get_default_illuminant(),
observer: Observer = get_default_observer(),
rgbs: RgbSpecification = get_default_rgb_specification(),
caa:
ChromaticAdaptationAlgorithm = get_default_chromatic_adaptation_algorithm(
),
is_scaled: bool = False):
"""
:param data: the color space data to contain
:param axis: the axis along which the color data lies. If `axis` is not
specified, then it will be determined automatically by finding the
last dimension with the required size.
:param illuminant: the illuminant
:param observer: the observer
:param rgbs: the rgb specification
:param caa: the chromatic adaptation algorithm
:param is_scaled: Whether or not the data is scaled
"""
if is_scaled:
self._data = np.array(data, copy=True) / self.scale_factor
else:
self._data = np.array(data, copy=True)
self._data.flags.writeable = False
self._axis = (axis if axis is not None else get_matching_axis(
self._data.shape, 3))
self._illuminant = (illuminant if illuminant is not None else
get_default_illuminant())
self._observer = (observer
if observer is not None else get_default_observer())
self._rgbs = (rgbs
if rgbs is not None else get_default_rgb_specification())
self._caa = (caa if caa is not None else
get_default_chromatic_adaptation_algorithm())
self._is_scaled = is_scaled
def spectrum2xyz(
spectrum: np.ndarray,
wavelengths: np.ndarray,
*,
axis: int = None,
illuminant: Illuminant = get_default_illuminant(),
observer: Observer = get_default_observer()
) -> np.ndarray:
"""
Convert reflectance spectrum to XYZ
:param spectrum: the reflectance spectrum
:param wavelengths: the wavelengths corresponding to the spectra
:param axis: The axis along which the spectra lie. If this is None,
then the axis is the last axis with a size that matches wavelengths.
:param illuminant: the illuminant
:param observer: the observer
:return:
"""
if axis is None:
axis = get_matching_axis(spectrum.shape, wavelengths.size)
# Need to ensure that we reshape all the 1-D parts of the integral, so that
# they can be broadcast properly
new_shape = [1] * len(spectrum.shape)
new_shape[axis] = -1
wavelengths = wavelengths.reshape(new_shape)
xbar = observer.get_xbar(wavelengths).reshape(new_shape)
ybar = observer.get_ybar(wavelengths).reshape(new_shape)
zbar = observer.get_zbar(wavelengths).reshape(new_shape)
illuminant_psd = illuminant.get_psd(wavelengths).reshape(new_shape)
norm_factor = (
1 / integrate.trapz(illuminant_psd * ybar, x=wavelengths, axis=axis))
phi = spectrum * illuminant_psd
# This is the shape that each XYZ component must be
component_shape = list(spectrum.shape)
component_shape[axis] = 1
def integrate_phi(bar: np.ndarray):
area = norm_factor * integrate.trapz(bar * phi, wavelengths, axis=axis)
# noinspection PyTypeChecker
return np.reshape(area, component_shape)
x = integrate_phi(xbar)
y = integrate_phi(ybar)
z = integrate_phi(zbar)
return np.concatenate((x, y, z), axis=axis)
def xyz2xyy(xyz: np.ndarray,
*,
axis: int = None,
illuminant: Illuminant = None,
observer: Observer = None) -> np.ndarray:
"""
Convert XYZ to xyY
:param xyz:
:param axis:
:param illuminant:
:param observer:
:return:
"""
if axis is None:
axis = get_matching_axis(xyz.shape, 3)
if illuminant is None:
illuminant = get_default_illuminant()
if observer is None:
observer = get_default_observer()
inds = construct_component_inds(axis, xyz.ndim, 3)
denominator = np.sum(xyz, axis, keepdims=True)
nzd = denominator != 0
xyy = np.zeros(xyz.shape)
if np.any(nzd):
xyy[inds[0]][nzd] = xyz[inds[0]][nzd] / denominator[nzd]
xyy[inds[1]][nzd] = xyz[inds[1]][nzd] / denominator[nzd]
xyy[inds[2]] = xyz[inds[1]]
if not np.all(nzd):
# For any point that is pure black (X=Y=Z=0), give it the
# chromaticity of the white point of the specified illuminant and
# observer.
white_point = illuminant.get_white_point(observer)
# to prevent infinite recursion, ensure that the white point is
# non-black
if white_point[1] > 0:
zd = np.logical_not(nzd)
white_point_xyy = xyz2xyy(white_point,
illuminant=illuminant,
observer=observer)
xyy[inds[0]][zd] = white_point_xyy[0]
xyy[inds[1]][zd] = white_point_xyy[1]
return xyy
def get_white_point(self, observer: Observer = None) -> np.ndarray:
"""
Calculate the white point of the illuminant with a specified observer
:param observer: The observer
:return: the white point
"""
if observer is None:
from chromathicity.defaults import get_default_observer
observer = get_default_observer()
wls = observer.wavelengths
p = self.get_psd(wls)
x_power = observer.xbar * p
is_valid_x = np.logical_not(np.isnan(x_power))
x_point = integrate.trapz(x_power[is_valid_x], wls[is_valid_x])
y_power = observer.ybar * p
is_valid_y = np.logical_not(np.isnan(y_power))
y_point = integrate.trapz(y_power[is_valid_y], wls[is_valid_y])
z_power = observer.zbar * p
is_valid_z = np.logical_not(np.isnan(z_power))
z_point = integrate.trapz(z_power[is_valid_z], wls[is_valid_z])
return np.array([x_point, y_point, z_point]) / y_point
def xyz2xyzr(
xyz: np.ndarray,
*,
axis: int = None,
illuminant: Illuminant = get_default_illuminant(),
observer: Observer = get_default_observer()
) -> np.ndarray:
"""
Convert XYZ to normalized XYZ reflectance
:param xyz: the raw xyz values
:param axis: the axis that the XYZ values lie along
:param illuminant: the illuminant
:param observer: the observer
:return: the xyz normalized Reflectance
"""
if axis is None:
axis = get_matching_axis(xyz.shape, 3)
new_shape = [1] * len(xyz.shape)
new_shape[axis] = -1
white_point = illuminant.get_white_point(observer).reshape(new_shape)
return xyz / white_point
def xyz2lrgb(xyz: np.ndarray,
*,
axis: int = None,
illuminant: Illuminant = None,
observer: Observer = None,
rgbs: RgbSpecification = None,
caa: ChromaticAdaptationAlgorithm = None) -> np.ndarray:
"""
Convert XYZ to linear RGB
:param xyz:
:param axis:
:param illuminant:
:param observer:
:param rgbs:
:param caa:
:return:
"""
if axis is None:
axis = get_matching_axis(xyz.shape, 3)
if illuminant is None:
illuminant = get_default_illuminant()
if observer is None:
observer = get_default_observer()
if rgbs is None:
rgbs = get_default_rgb_specification()
if caa is None:
caa = get_default_chromatic_adaptation_algorithm()
# If the white points are not equal, we will need to convert to the
# RGB white point
source_white_point = illuminant.get_white_point(observer)
destination_white_point = rgbs.white_point
if not np.allclose(
source_white_point, destination_white_point, rtol=1e-5,
atol=1e-14):
xyz = xyz2xyz(xyz, source_white_point, destination_white_point, axis,
caa)
# Get the XYZ values into the correct shape for matrix multiplication
n_dims = xyz.ndim
new_dims = list(range(n_dims))
if axis != n_dims - 1:
new_dims[-1] = axis
new_dims[axis] = n_dims - 1
xyz = xyz.transpose(new_dims)
input_shape = xyz.shape
xyz_is_not_matrix = xyz.ndim != 2
if xyz_is_not_matrix:
xyz = xyz.reshape((-1, 3))
# Convert to linear RGB
m = rgbs.linear_transformation
lrgb = np.linalg.solve(m.T, xyz.T).T
# Transform the destination data back to the original shape
if xyz_is_not_matrix:
lrgb = lrgb.reshape(input_shape)
return lrgb.transpose(new_dims)
def lrgb2xyz(lrgb: np.ndarray,
*,
axis: int = None,
illuminant: Illuminant = None,
observer: Observer = None,
rgbs: RgbSpecification = None,
caa: ChromaticAdaptationAlgorithm = None) -> np.ndarray:
"""
Convert from linear RGB to XYZ
:param lrgb:
:param axis:
:param illuminant:
:param observer:
:param rgbs:
:param caa:
:return:
"""
if axis is None:
axis = get_matching_axis(lrgb.shape, 3)
if illuminant is None:
illuminant = get_default_illuminant()
if observer is None:
observer = get_default_observer()
if rgbs is None:
rgbs = get_default_rgb_specification()
if caa is None:
caa = get_default_chromatic_adaptation_algorithm()
# Get the XYZ values into the correct shape for matrix multiplication
n_dims = lrgb.ndim
new_dims = list(range(n_dims))
if axis != n_dims - 1:
new_dims[-1] = axis
new_dims[axis] = n_dims - 1
lrgb = lrgb.transpose(new_dims)
input_shape = lrgb.shape
lrgb_is_not_matrix = n_dims != 2
if lrgb_is_not_matrix:
lrgb = lrgb.reshape((-1, 3))
# Do the transformation
m = rgbs.linear_transformation
xyz = lrgb.dot(m)
# Transform back to the original shape
if lrgb_is_not_matrix:
xyz = xyz.reshape(input_shape)
if axis != n_dims - 1:
xyz = xyz.transpose(new_dims)
source_white_point = rgbs.white_point
destination_white_point = illuminant.get_white_point(observer)
if not np.allclose(
source_white_point, destination_white_point, rtol=1e-5,
atol=1e-14):
return xyz2xyz(xyz, source_white_point, destination_white_point, axis,
caa)
else:
return xyz